Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder.

Background: An imbalance between model-based and model-free decision-making systems is a common feature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive features associated with model-based and model-free systems in IGD and alcohol use disorder (AUD). Method: Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group. Results: The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE. Discussion and Conclusions: The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

Pharmacological and non-pharmacological interventions for irritability in autism spectrum disorder: a systematic review and meta-analysis with the GRADE assessment.

BACKGROUND: Numerous interventions for irritability in autism spectrum disorder (ASD) have been investigated. We aimed to appraise the magnitude of pharmacological and non-pharmacological interventions for irritability in ASD without any restrictions in terms of eligible interventions. METHODS: We systematically searched PubMed/MEDLINE, Scopus, and Web of Science until April 15, 2023. We included randomized controlled trials (RCTs) with a parallel design that examined the efficacy of interventions for the treatment of irritability in patients of any age with ASD without any restrictions in terms of eligible interventions. We performed a random-effects meta-analysis by pooling effect sizes as Hedges' g. We classified assessed interventions as follows: pharmacological monotherapy, risperidone plus adjuvant therapy versus risperidone monotherapy, non-pharmacological intervention, and dietary intervention. We utilized the Cochrane tool to evaluate the risk of bias in each study and the GRADE approach to assess the certainty of evidence for each meta-analyzed intervention. RESULTS: Out of 5640 references, we identified 60 eligible articles with 45 different kinds of interventions, including 3531 participants, of which 80.9% were males (mean age [SD] = 8.79 [3.85]). For pharmacological monotherapy, risperidone (Hedges' g - 0.857, 95% CI - 1.263 to - 0.451, certainty of evidence: high) and aripiprazole (Hedges' g - 0.559, 95% CI - 0.767 to - 0.351, certainty of evidence: high) outperformed placebo. Among the non-pharmacological interventions, parent training (Hedges' g - 0.893, 95% CI - 1.184 to - 0.602, certainty of evidence: moderate) showed a significant result. None of the meta-analyzed interventions yielded significant effects among risperidone + adjuvant therapy and dietary supplementation. However, several novel molecules in augmentation to risperidone outperformed risperidone monotherapy, yet from one RCT each. LIMITATIONS: First, various tools have been utilized to measure the irritability in ASD, which may contribute to the heterogeneity of the outcomes. Second, meta-analyses for each intervention included only a small number of studies and participants. CONCLUSIONS: Only risperidone, aripiprazole among pharmacological interventions, and parent training among non-pharmacological interventions can be recommended for irritability in ASD. As an augmentation to risperidone, several novel treatments show promising effects, but further RCTs are needed to replicate findings. Trial registration PROSPERO, CRD42021243965.

Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models.

Introduction: Previous studies have investigated predictive factors for parenting stress in caregivers of autism spectrum disorder (ASD) patients using traditional statistical approaches, but their study settings and results were inconsistent. Herein, this study aimed to identify major predictors for parenting stress in this population by developing explainable machine learning models. Methods: Study participants were collected from the Department of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, the Republic of Korea between March 2016 and October 2020. A total of 36 model features were used, which include subscales of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) for caregivers' psychopathology, Social Responsiveness Scale-2 for core symptoms, and Child Behavior Checklist (CBCL) for behavioral problems. Machine learning classifiers [eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression, and support vector machine (SVM) classifier] were generated to predict severe total parenting stress and its subscales (parental distress, parent-child dysfunctional interaction, and difficult child). Model performance was assessed by area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. We utilized the SHapley Additive exPlanations tree explainer to investigate major predictors. Results: A total of 496 participants were included [mean age of ASD patients 6.39 (SD 2.24); 413 men (83.3%)]. The best-performing models achieved an AUC of 0.831 (RF model; 95% CI 0.740-0.910) for parental distress, 0.814 (SVM model; 95% CI 0.720-0.896) for parent-child dysfunctional interaction, 0.813 (RF model; 95% CI 0.724-0.891) for difficult child, and 0.862 (RF model; 95% CI 0.783-0.930) for total parenting stress on the test set. For the total parenting stress, ASD patients' aggressive behavior and anxious/depressed, and caregivers' depression, social introversion, and psychasthenia were the top 5 leading predictors. Conclusion: By using explainable machine learning models (XGBoost and RF), we investigated major predictors for each subscale of the parenting stress index in caregivers of ASD patients. Identified predictors for parenting stress in this population might help alert clinicians whether a caregiver is at a high risk of experiencing severe parenting stress and if so, providing timely interventions, which could eventually improve the treatment outcome for ASD patients.

Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs.

Importance: Screening for autism spectrum disorder (ASD) is constrained by limited resources, particularly trained professionals to conduct evaluations. Individuals with ASD have structural retinal changes that potentially reflect brain alterations, including visual pathway abnormalities through embryonic and anatomic connections. Whether deep learning algorithms can aid in objective screening for ASD and symptom severity using retinal photographs is unknown. Objective: To develop deep ensemble models to differentiate between retinal photographs of individuals with ASD vs typical development (TD) and between individuals with severe ASD vs mild to moderate ASD. Design, Setting, and Participants: This diagnostic study was conducted at a single tertiary-care hospital (Severance Hospital, Yonsei University College of Medicine) in Seoul, Republic of Korea. Retinal photographs of individuals with ASD were prospectively collected between April and October 2022, and those of age- and sex-matched individuals with TD were retrospectively collected between December 2007 and February 2023. Deep ensembles of 5 models were built with 10-fold cross-validation using the pretrained ResNeXt-50 (32×4d) network. Score-weighted visual explanations for convolutional neural networks, with a progressive erasing technique, were used for model visualization and quantitative validation. Data analysis was performed between December 2022 and October 2023. Exposures: Autism Diagnostic Observation Schedule-Second Edition calibrated severity scores (cutoff of 8) and Social Responsiveness Scale-Second Edition T scores (cutoff of 76) were used to assess symptom severity. Main Outcomes and Measures: The main outcomes were participant-level area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The 95% CI was estimated through the bootstrapping method with 1000 resamples. Results: This study included 1890 eyes of 958 participants. The ASD and TD groups each included 479 participants (945 eyes), had a mean (SD) age of 7.8 (3.2) years, and comprised mostly boys (392 [81.8%]). For ASD screening, the models had a mean AUROC, sensitivity, and specificity of 1.00 (95% CI, 1.00-1.00) on the test set. These models retained a mean AUROC of 1.00 using only 10% of the image containing the optic disc. For symptom severity screening, the models had a mean AUROC of 0.74 (95% CI, 0.67-0.80), sensitivity of 0.58 (95% CI, 0.49-0.66), and specificity of 0.74 (95% CI, 0.67-0.82) on the test set. Conclusions and Relevance: These findings suggest that retinal photographs may be a viable objective screening tool for ASD and possibly for symptom severity. Retinal photograph use may speed the ASD screening process, which may help improve accessibility to specialized child psychiatry assessments currently strained by limited resources.